
"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a       :						Tuple       		=		{
    '''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],
    '''convert_funnel_original_tf_checkpoint_to_pytorch''': [],
    '''tokenization_funnel''': ['''FunnelTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a       :						Tuple       		=		['''FunnelTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a       :						Union[str, Any]       		=		[
     '''FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''FunnelBaseModel''',
     '''FunnelForMaskedLM''',
     '''FunnelForMultipleChoice''',
     '''FunnelForPreTraining''',
     '''FunnelForQuestionAnswering''',
     '''FunnelForSequenceClassification''',
     '''FunnelForTokenClassification''',
     '''FunnelModel''',
     '''FunnelPreTrainedModel''',
     '''load_tf_weights_in_funnel''',
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 a       :						Dict       		=		[
     '''TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''TFFunnelBaseModel''',
     '''TFFunnelForMaskedLM''',
     '''TFFunnelForMultipleChoice''',
     '''TFFunnelForPreTraining''',
     '''TFFunnelForQuestionAnswering''',
     '''TFFunnelForSequenceClassification''',
     '''TFFunnelForTokenClassification''',
     '''TFFunnelModel''',
     '''TFFunnelPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
 from .tokenization_funnel import FunnelTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_funnel_fast import FunnelTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_funnel import (
      FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
      FunnelBaseModel,
      FunnelForMaskedLM,
      FunnelForMultipleChoice,
      FunnelForPreTraining,
      FunnelForQuestionAnswering,
      FunnelForSequenceClassification,
      FunnelForTokenClassification,
      FunnelModel,
      FunnelPreTrainedModel,
      load_tf_weights_in_funnel,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_funnel import (
      TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFFunnelBaseModel,
      TFFunnelForMaskedLM,
      TFFunnelForMultipleChoice,
      TFFunnelForPreTraining,
      TFFunnelForQuestionAnswering,
      TFFunnelForSequenceClassification,
      TFFunnelForTokenClassification,
      TFFunnelModel,
      TFFunnelPreTrainedModel,
  )

else:
 import sys

 a       :						Union[str, Any]       		=		_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

"""simple docstring"""



import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union

import torch

from ..utils import BaseOutput


a       :						int       		=		'''scheduler_config.json'''




class 		a_    (			_UpperCAmelCase						):
    a   :					List[Any]			     =					1
    a   :					Tuple			     =					2
    a   :					Dict			     =					3
    a   :					str			     =					4
    a   :					Optional[int]			     =					5
    a   :					Any			     =					6
    a   :					int			     =					7
    a   :					Any			     =					8
    a   :					List[Any]			     =					9
    a   :					Any			     =					10
    a   :					List[str]			     =					11
    a   :					Optional[int]			     =					12
    a   :					Any			     =					13
    a   :					Dict			     =					14







@dataclass
class 		a_    (			_UpperCAmelCase						):
    a   :					torch.FloatTensor







class 		a_    :
    a   :					Dict			     =					SCHEDULER_CONFIG_NAME
    a   :					List[str]			     =					[]
    a   :					Optional[int]			     =					True







    @classmethod
    def   _snake_case       (      cls      :							List[str]  ,       __UpperCamelCase      :							Dict[str, Any] = None  ,       __UpperCamelCase      :							Optional[str] = None  ,       __UpperCamelCase      :							List[str]=False  ,       **__UpperCamelCase      :							int  ,       )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = cls.load_config(
            pretrained_model_name_or_path=__UpperCamelCase  ,       subfolder=__UpperCamelCase  ,       return_unused_kwargs=__UpperCamelCase  ,       return_commit_hash=__UpperCamelCase  ,       **__UpperCamelCase  ,       )
        return cls.from_config(__UpperCamelCase  ,       return_unused_kwargs=__UpperCamelCase  ,       **__UpperCamelCase  )







    def   _snake_case       (      self      :							Tuple  ,       __UpperCamelCase      :							Union[str, os.PathLike]  ,       __UpperCamelCase      :							bool = False  ,       **__UpperCamelCase      :							List[str]  )		->Any:






        '''simple docstring'''






        self.save_config(save_directory=__UpperCamelCase  ,       push_to_hub=__UpperCamelCase  ,       **__UpperCamelCase  )







    @property
    def   _snake_case       (      self      :							Optional[int]  )		->List[Any]:






        '''simple docstring'''






        return self._get_compatibles()







    @classmethod
    def   _snake_case       (      cls      :							List[str]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = list(set([cls.__name__] + cls._compatibles  )  )
        _UpperCAmelCase        = importlib.import_module(__name__.split("""."""  )[0]  )
        _UpperCAmelCase        = [
            getattr(__UpperCamelCase  ,       __UpperCamelCase  ) for c in compatible_classes_str if hasattr(__UpperCamelCase  ,       __UpperCamelCase  )
        ]
        return compatible_classes